基于无人机高光谱遥感的春玉米氮营养指数反演

Hyperspectral technology can accurately detect the status of crop nitrogen nutrition. Taking the Northeast spring corn as the sample,the random test data of 6 nitrogen fertilizer gradients were obtained,using UHD 185 hyperspectral imaging system,the hyperspectral remote sensing images of spring maize canopy in the experimental plot of key growth period were obtained,and the extracted hyperspectral information of canopy was preprocessed by five methods. The inversion model of nitrogen nutrition index of spring maize was constructed by using partial least squares regression,BP neural network and random forest algorithm. The conclusions were as follows:(1)In the Near-infrared band,the hyperspectral reflectivity had high correlation with agronomic parameters under different preprocessing methods. Above ground biomass and nitrogen nutrition index were highly correlated with spectral reflectance and characteristic parameters. (2) The model was constructed with SMC pretreated by hyperspectral characteristic parameters. The average R² of the prediction set reached 0. 80,which was higher than other preprocessing methods. (3) The random forest algorithm combined with the MSC pre-processing method to invert the nitrogen nutrition index of corn had better effect,higher accuracy,and more stable model. In this study,by comparing the diagnostic model of spring maize nitrogen nutrition established by different methods,it ensured the rapidity,accuracy and standardization of the inversion of maize nitrogen nutrition were ensured,which laid the technical foundation and theoretical basis for precise fertilization of maize.